Data is the new oil, you must have heard this adage, but what will you do with oil if you cannot store and process it? Well this is where the oil containers, a.k.a data warehouses, data lakes and data lakehouses come into picture.
A data warehouse stores data in an accessible manner and a data lake provides the luxury of massive storage capacity. One fine day some data-Archimedes had a Eureka moment and decided that why don’t we combine the power of both these data storage architecture systems?
This idea led to the birth of a data lakehouse which is a combination of a data warehouse and a data lakehouse. A growing number of organizations today are investing heavily into a new data storage solution known as a data lakehouse.
This article focuses on the advantages and limitations of using a data lakehouse, which combines the benefits of data lakes and data warehouses. It also provides insights into the types of organizations that can benefit most from using a data lakehouse solution.
How data lakehouses work?
Data is ingested from different sources into a data lakehouse where it is collected and loaded into the system. The data loaded into a data lakehouse can come in a variety of forms like structured, unstructured and semi-structured data. The data sources can be many like social media posts, IoT sensor data, data from blog posts, voice data from customer service recordings and so on.
Once the data ingestion stage is finished, the next stage is that of data transformation. In this stage the ingested data is cleaned, homogenized and enriched with relevant data. This is the stage where the quality and consistency of the ingested data is increased.
After the data transformation stage is complete, the transformed data is now stored in a data repository. With data lakehouses, you get a wide range of data storage options like file storage, object storage and columnar databases. All these file storage options help you store data in an economical and efficient manner.
Now let’s take a look at the advantages of data lakehouses.
Advantages of Data Lakehouses
- Increased flexibility and scalability
- Reduced data latency
- Lower cost compared to traditional data warehouse solutions
- Ability to handle structured, unstructured, and semi-structured data
- Integration with existing big data tools and platforms
Challenges of Data Lakehouses
- Requires significant technical expertise to set up and maintain
- Risk of becoming a data dumping ground
Is a Data Lakehouse Right for Your Organization?
Whether to go with a data lakehouse or not depends on your specific use case. Here are a few use cases in which choosing a data lakehouse can prove to be the best solution.
When you have mixed workloads
A data lakehouse might be a better option than either a data lake or a data warehouse if you need to serve standard business intelligence (BI) applications as well as data science activities. A data lakehouse combines the speed and SQL capabilities of a data warehouse with the low overhead and adaptability of a data lake.
A data lakehouse allows businesses to centrally store and analyze both structured data from data warehouse transactions and unstructured data from social media and other sources. This paves the way for a richer understanding of client habits and preferences, which in turn improves service and drives more business.
Example:-To better serve its clientele, a retail firm can consider using a data lakehouse for integrating data science with more conventional business intelligence techniques
When you want to eliminate data silos
A data lakehouse might help you eliminate data silos if your company is having trouble doing so. It can help you wipe out the inconsistencies in data, eliminating the need for intricate ETL procedures lessened as a result.
By aggregating all of an organization's data in one place and granting authorized access to specific groups or individuals, a "data lakehouse" helps businesses save time and money. Better patient care and results are the result of the elimination of data silos and the improvement of data consistency made possible by this.
Example:- A data lakehouse can prove to be very effective for instance in a hospital chain spread across the country. Information may be inconsistent across different divisions of the hospital due to data silos that prevent information from being shared.
When you require high data security and compliance
A data lakehouse could be the best option for your company if it has to follow stringent data security and compliance requirements. The reason for this is because a data lakehouse can provide data lineage and auditing capabilities, as well as give better control over access to sensitive data.
Data encryption, granular access controls, and audit logs are just a few of the additional security features that can be made available by a data lake house.This helps the organization adhere to various industry-specific as well as general laws like GDPR, HIPAA, and PCI-DSS, which all require strict data protection measures.
Example: A bank that must safeguard customer information while adhering to applicable regulations can benefit from using a data lakehouse.
When your use case requires a lot of data analytics
From business intelligence (BI) to artificial intelligence (AI), a lakehouse is built to accommodate a wide variety of data management and analytics use cases (AI). When you have access to a lakehouse, you can process massive amounts of data using advanced analytics like machine learning and deep learning.
Because of this adaptability, organizations may more easily mine their data for insights and use those findings as a springboard for new product development.
Example:- By storing and processing patient data in a lakehouse, a healthcare provider may do sophisticated analytics like illness prediction and therapy optimization.
Conclusion
As a developer, if your organization deals with large amounts of data, and you're looking for a more flexible, scalable, and cost-effective way to manage and analyze that data, a data lakehouse could be the right choice for you. So, don't hesitate to explore the possibilities of a data lakehouse solution for your big data needs.
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